Objective pain signal acquisition system and processed signal

ABSTRACT

Methods, apparatuses and systems relating to the objective measurement of the subjective perception of pain in a subject are disclosed. In one aspect, a system for objectively measuring a subjective perception of pain by a subject comprises a plurality of electrodes, including a left channel electrode and a right channel electrode. The plurality of electrodes measures electrical activity at a respective plurality of sites on the subject to generate at least two sets of electrical activity measurements. The system further comprises a processor for processing the at least two sets of electrical activity measurements into at least two normalized signals, and comparing the at least two normalized signals to each other to identify the presence of pain in the subject.

This application is a continuation application of U.S. patentapplication Ser. No. 09/899,824, filed on Jul. 5, 2001, entitled“Objective Pain Measurement System And Method,” which claims benefit ofU.S. Provisional application No. 60/216,464, filed Jul. 6, 2000, and ofU.S. Provisional Application No. 60/241,722, filed on Oct. 18, 2000.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The field of the present invention relates to medical diagnostic tools.More particularly, the field of the present invention relates to systemsand methods relating to measuring and reporting a subject's pain.

2. Background

Pain is an unpleasant sensation, ranging from slight discomfort tointense suffering. But because to a great extent pain is a subjectivephenomenon, it has frequently defied objective, quantitativemeasurement. Traditionally, physicians have had to assess a patient'spain by relying on the patient's own description of it. Butself-description is not only subjective by definition, it is ofteninaccurate, in part because it is difficult for subjects to preciselyarticulate their pain while in the midst of a pain experience.

Moreover, objective assessment of pain is all but impossible insituations where the patient is not fully communicative, such as whenthe patient is an infant, the patient is not fully conscious orcoherent, or the patient is a non-human.

Today, uni-dimensional scales are used to quantify pain. These scalesfrequently employ verbal (mild, moderate, severe) and numerical (0-10)ratings. Today's caregivers also use multidimensional scales along withcomplex, pain diagnosis questionnaires designed to extract as muchsubjective information as possible from the subject (e.g. sensory,emotional and cognitive).

These pain quantification methods are used in a number of settings. Mostcommonly, physicians and other health care professionals apply thesemethods to diagnose and/or treat a patient. Physicians may also usethese methods to track the progress of a patient's illness over time orto determine an amount of pain medication to prescribe to a patient. Inother settings, these methods are used to test the efficacy of certainpain-relieving drugs and to establish standard dosages for them.Nonetheless, these methods often lead to inaccurate conclusions becauseof the subjective nature of the assessment inherent in them.

Logically, pain assessment plays a vital role in determining the amountof pain medication to give a patient. As a result, hospital staff andother health care providers also use visual clues to assess theintensity of a patient's pain and determine the amount of painmedication to provide. Under this visual assessment method, thecaregiver will commonly use a visual analog scale (VAS), usually scoredfrom 1 to 10, to rate a patient's pain intensity. In a typical scenario,the caregiver will consider different clues to score the patient's painintensity, such as facial expressions and cardio-respiratory function,in addition to patient statements of satisfaction.

Notwithstanding the healthcare providers' diligence, studies have shownthat professional caregivers usually give too much or too little painmedication to patients evaluated with these visual scoring methods.Importantly, a caregiver's failure to give enough pain medication maynot only reduce a patient's quality of life, but may compromise apatient's ability to fight disease, cause or complicate physiologicaldisorders, and even hasten death. On the other hand, caregivers thatovermedicate patients can also cause harmful side effects, including, inextreme cases, patient respiratory arrest.

Some patients also intentionally misrepresent the existence or extent oftheir pain. These misrepresentations may stem from financial orfiduciary incentives (including a desire for disability payments orinsurance damage settlements), chemical dependencies on painmedications, or other patient-perceived secondary benefits to obtainingpain medication. Regardless of the motivation, patient misrepresentationaccounts for a significant portion of the demand for pain medicationprescriptions. Yet, without any reliable basis for denying suchprescriptions, physicians generally must assume that the claims aretruthful, even when they may suspect a lack of sincerity. Otherwise, thecaregiver may be accused of inhumane treatment. Conversely, otherpatients may underreport their pain, again for a variety of reasons.

Despite these inaccurate representations, hospitals and other healthcaregivers often provide patients with a class of devices known as PatientControlled Analgesia (PCA) devices. PCA devices employ a type ofanalgesia system that enables the patient, often in a post-operativesetting, to self-administer pain medicine.

Commercial PCA devices include devices such as the Atom PCA Pump 500,APII, Deltec CADD-PCA 5800, Sabratek 6060 and the Verifuse. In a commonform of PCA, the patient is provided with a mechanical apparatuscomprised of a reservoir and a patient-operable pump. On patient demand,the pump dispenses incremental doses of pain medicine from the resevoirinto the patient's intravenous (IV) system. The device may also comprisea lock-out interval feature that prevents patient remedication for aperiod of time so as to ensure against over-medication.

While caregivers using VAS methods cannot consistently provide the rightamount of pain medication to patients, studies have likewise shown thata patient's own assessment of satisfaction, even when used in connectionwith a PCA device, does not reliably indicate when to deliver painmedication. One study shows that although patients may feel satisfied bya regimen of self-administered pain therapy, the majority of those samepatients are self-treated below their individual subjective painthresholds. Forst et. al., Archives of Orthopaedic and Trauma Surgery(Germany), v. 119, p. 267-270, (1999). Moreover, the act ofself-medication itself has been found to be unimportant to the issue ofpatient satisfaction when the patient has sufficient pain relief throughmedication. Chumbley, et al., Anesthesia (England), v. 54 (4), p. 386-9(1999).

The present PCA methods and systems also have other drawbacks. Forexample, they cannot be readily used, if at all, for infants, toddlers,certain spinal cord patients, and others who cannot operate the deviceor are unable to understand the instructions for controlling the PCA.Also, current PCA devices do not normalize people's responses, therebymaking the subjective nature of pain self-assessment a factor in theoperation of the PCA. Even in honest attempts to be objective, patientsmay rate the same subjective experience of pain differently. Forexample, one person may rate a certain subjective sensation of pain a“10” on the VAS scale whereas another person may rate the same or asimilar subjective sensation of pain a “5” depending on a variety ofpsychological factors and life experiences. Thus, without a means tonormalize patient self-assessment, PCA devices rely on subjectivepsychological factors as much as on the type of illness to determine howmuch pain medicine to provide.

Moreover, self-assessment may lead to inconsistent treatment betweendifferent patient types. For example, children who use PCA devices havebeen reported to frequently experience nausea and vomiting as a resultof overdoses, as compared with adults. PCA devices also do not typicallyreduce the burden on caregivers because, in many cases, the caregiversmust repeatedly instruct patients on how to use the PCA devices andmonitor their use.

In contrast, previous efforts in pain research have attempted toidentify physiological phenomena related to the subjective sensation ofpain. Heart-rate, blood pressure, perspiration and skin conductance aresome of the physiological phenomena that have been found to be affectedby pain. But these physiological phenomena have also been found to benon-specific to pain and, in fact, have been used in other applications,such as polygraphy. Furthermore, these physiological phenomena tend tohabituate quickly. Consequently, they are inadequate for objectivelyassessing pain.

U.S. Pat. No. 6,018,675 issued to Apkarian et al. discloses a painmeasurement system based on comparative functional magnetic resonanceimaging (MRI) of the brain of a subject. In the disclosed system,measurements quantifying a subject's pain level are made by comparingimages of the subject's brain when the subject is in pain with thecorresponding brain images made when the subject is not in pain. Thesystem therefore generally requires a baseline, pain-free brain imagefor each subject. Futhermore, the functional MRI-based measurementsystem is generally a large piece of machinery, is not portable andrequires a substantial infrastructure, including trained personnel tooperate.

SUMMARY OF THE INVENTION

The present invention provides, in one aspect, systems and methods forobjectively assessing a subject's subjective perception of pain.

In a second separate aspect, the present invention is a systemcomprising a plurality of sensors for measuring electrical activity at arespective plurality of sites on the subject in order to generate a setof electrical activity measurements. The system further comprises aprocessor for processing the set of electrical activity measurementsinto a normalized signal, and determining a level value representativeof an objective pain measurement for the normalized signal within apredetermined range of frequencies.

In a third separate aspect, the present invention comprises a specificmethod of objectively measuring a level of pain subjectively perceivedby a subject. The method preferably includes the steps of selecting aplurality of sites on the subject for sensing electrical activity,making electrical activity measurements for the plurality of sites,processing the electrical activity measurements into a normalizedsignal, and determining a level value for the normalized signal within apredetermined range of frequencies.

In a fourth separate aspect, the present invention comprises acomputer-readable medium on which are stored sequences of instructionsfor objectively measuring a subjective perception of pain in a subject.The sequences of instructions are for performing the steps in the methodof the third aspect identified above.

In a fifth separate aspect, the present invention is a system comprisingmeans for measuring a subject's electrical activity at a plurality ofsites in order to generate a set of electrical activity measurements.The system further includes processing means for processing the set ofelectrical activity measurements into a normalized signal, determining alevel value for the normalized signal within a predetermined range offrequencies, and scaling the value for the signal into an objective painmeasurement.

In a sixth separate aspect, the present invention is a system comprisinga plurality of sensors, including a left channel electrode and a rightchannel electrode. The plurality of sensors measures a subject'selectrical activity at a respective plurality of sites in order togenerate at least two sets of electrical activity measurements. Thesystem further comprises a processor for processing the at least twosets of electrical activity measurements into at least two normalizedsignals, and comparing the at least two normalized signals to each otherin order to identify the presence of pain in the subject.

In a seventh separate aspect, the present invention comprises a specificmethod of objectively measuring a level of pain subjectively perceivedby a subject. The method preferably includes the steps of selecting aplurality of sites on the subject for sensing electrical activity,making electrical activity measurements for the plurality of sites,processing the electrical activity measurements into at least twonormalized signals, and comparing the at least two normalized signals toeach other in order to identify the presence of pain in the subject.

In an eighth separate aspect, the present invention comprises acomputer-readable medium on which are stored sequences of instructionsfor objectively measuring a subjective perception of pain in a subject.The sequences of instructions are for performing the steps in the methodof the seventh aspect identified above.

In a ninth separate aspect, the present invention is a system comprisingmeans for measuring a subject's electrical activity at a respectiveplurality of sites in order to generate at least two sets of electricalactivity measurements. The system further comprises means for processingthe at least two sets of electrical activity measurements into at leasttwo normalized signals, and comparing the at least two normalizedsignals to each other in order to identify the presence of pain in thesubject.

In a tenth separate aspect, the present invention comprises a networkfor objectively measuring pain subjectively perceived by one or moresubjects. The network preferably includes at least one signalacquisition subsystem for making electrical activity measurements at asite on each of the one or more subjects, a signal processing subsystemfor analyzing the electrical activity measurements and determininganalysis values representing different periods of time, and acommunication channel linking the signal processing subsystem and the atleast one signal acquisition subsystem in order to transmit thesubjects' electrical activity measurements to the signal processingsubsystem.

In an eleventh separate aspect, the present invention comprises a painmeasurement report comprising a reference to a subject and a value orseries of values representing an objective level of pain subjectivelyexperienced by the subject.

In a twelfth separate aspect, the present invention comprises a methodof operating a network based on the analysis of pain-related electricalactivity measurements. The method preferably comprises the steps ofreceiving electrical activity measurements on a subject from a testinglocation, analyzing the electrical activity measurements to obtain anobjective pain measurement report, transmitting the objective painmeasurement report to the testing location, and receiving non-medicalpatient information, including, for example, the number of reports,insurance information, incurred costs, patient contact information,patient histories, and patient feedback.

In a thirteenth separate aspect, the present invention comprises anacquisition system for acquiring an objective signal representative of asubjective perception of pain experienced by a subject. The acquisitionsystem preferably comprises a sensor array for measuring an electricalsignal at a site on the subject, an amplifier for amplifying the signal,and a band-pass filter for substantially removing components of thesignal below about 0.1 Hertz and above about 5 Hertz.

In a fourtheenth separate aspect, the present invention comprises amethod of acquiring a signal representative of a subjective perceptionof pain experienced by a subject. The method preferably includes thesteps of detecting an electrical signal at a site on the subject,amplifying the signal, and filtering the signal to substantially removecomponents of the signal below about 0.1 Hertz and above about 5 Hertz.

In a fifteenth separate aspect, the present invention comprises a systemfor processing electrical activity measurements taken from a subject.The system comprises a memory for storing the electrical activitymeasurements and a processor for processing the electrical activitymeasurements into a normalized signal. A processor is also provided todetermine a level value for the normalized signal within a predeterminedrange of frequencies and to scale the level value for the signal into anobjective pain level.

In a sixteenth separate aspect, the present invention comprises aspecific method of processing electrical activity measurements takenfrom a subject. The method comprises the steps of processing theelectrical activity measurements into a normalized signal, determining alevel value for the normalized signal within a predetermined range offrequencies, and scaling the level value for the signal into anobjective pain measurement.

In a seventeenth separate aspect, the present invention comprises asensor array for measuring electrical activity on the forehead of asubject. The sensor array preferably includes a sensor pad, a leftchannel electrode positioned proximal to a left edge of the sensor pad,a right channel electrode positioned proximate to a right edge of thesensor pad, a common electrode positioned equidistant from the leftchannel electrode and the right channel electrode, and filteringcircuitry electrically connected to the electrodes in order to filtersignals from the electrodes in the range of about 0.1 Hertz to about 5Hertz.

In an eighteenth separate aspect, the present invention comprises aphysiological monitor for measuring multiple physiological signs of asubject. The physiological monitor preferably comprises a system forobjectively measuring a subjective perception of pain, in combinationwith any one or more of a thermometer, a pulse meter, a blood pressuregauge and a respiratory gauge.

In a nineteenth separate aspect, the present invention is a system fordelivering medication for reducing pain in a subject. The systempreferably comprises a reservoir for containing the medication, adelivery device connected to the reservoir for delivering the medicationto the subject, a delivery counter (connected to the reservoir) formeasuring the amount of medication transferred between the reservoir andthe delivery device, and an objective pain measurement device forobjectively measuring a subjective perception of pain experienced by thesubject. The system preferably further includes a medication deliverycontroller in communication with the objective pain measurement device,the delivery counter and the delivery device. The medication deliverycontroller preferably controls the amount of medication delivered to thesubject by the delivery device based on a delivery rate communicated bythe delivery counter and an objective pain measurement communicated bythe objective pain measurement device.

In a twentieth separate aspect, the present invention is an electricalsignal containing information objectively describing an intensity of asubjective experience of pain in a subject. The electrical signal isobtained by a process comprising the steps of selecting a site on thesubject for sensing electrical activity, detecting electrical activityfrom the site, and filtering the electrical activity within a frequencyrange of about 0.1 Hertz to about 5 Hertz.

The foregoing methods may be implemented in the form of systems,devices, and computer-readable media. Further embodiments as well asmodifications, variations and enhancements of the invention are alsodescribed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram generally depicting a preferred embodiment of asystem for measuring pain in a subject, the system being generallyreferred to herein as an objective pain measurement (OPM) system.

FIG. 2 is a diagram illustrating, by way of example, a preferredembodiment of an objective pain measurement system implemented as anetwork wherein a centralized signal processing subsystem is networkedto a plurality of signal acquisition subsystems 10 as generally depictedin FIG. 1.

FIG. 3 illustrates, by way of example, a physiological signal measuringdevice for objectively measuring blood pressure, pulse, temperature,respiration and pain level.

FIG. 4 is a diagram detailing a preferred embodiment of a signalacquisition subsystem, such as is depicted generally in FIG. 1.

FIG. 5 is a diagram illustrating one example of a sensor array, in theform of a sensor strip, for application, preferably, on the forehead ofa subject.

FIG. 6 is a diagram detailing an embodiment of a signal acquisitionsubsystem, such as is depicted generally in FIG. 1.

FIG. 7 is a diagram illustrating another example of a sensor array forapplication, preferably, on the forehead of a subject, such as isgenerally depicted in the signal acquisition subsystem of FIG. 6.

FIG. 8 is a diagram illustrating another example of a sensor array forapplication, preferably, on the forehead of a subject, such as isgenerally depicted in the signal acquisition subsystem of FIG. 6.

FIG. 9 is a diagram illustrating an exploded view of an example of asensor array for application, preferably, on the forehead of a subject,such as is generally depicted in the signal acquisition subsystem ofFIG. 6.

FIG. 10 is a diagram illustrating another example of a sensor array forapplication preferably on the forehead of a subject, such as isgenerally depicted in the signal acquisition subsystem of FIG. 6.

FIG. 11 is a block diagram illustrating one hardware configuration of asignal processing subsystem, such as that generally depicted in FIG. 1.

FIG. 12 is a functional block diagram illustrating one preferredembodiment of a signal processing subsystem, such as that generallydepicted in FIG. 1.

FIG. 13 is a process flow diagram illustrating one embodiment of asignal preparation method, such as may be performed by the signalpreparer represented in FIG. 12.

FIG. 14 is a process flow diagram illustrating one embodiment of a painintensity quantification method, such as may be performed by the painintensity quantifier represented in FIG. 12.

FIG. 15 is a graph depicting a first example of a typical resultcomparing objective pain level monitoring versus subjective painreporting. The graph demonstrates a correlation between the subjectivereport and the objective reading.

FIG. 16 is a graph depicting a second example comparing objective painlevel monitoring with subjective reporting, thereby clarifying thebenefit of using confidence information for rejection of artifactualpain readings.

FIG. 17 is a graph illustrating pain level during labor betweencontractions and the progression of pain during a uterine contraction,starting with baseline level, rising to peak value at contractionclimax, and returning to baseline level with relaxation.

FIG. 18 is a diagram graphically depicting a first protocol(Baseline-Pain) for a study of the performance of an Objective PainMeasurement (OPM) system on a set of subjects.

FIG. 19 is a diagram graphically depicting a second protocol (Step) fora study of the performance of an Objective Pain Measurement (OPM) systemon a set of subjects.

FIG. 20 is a graph depicting a typical result of applying the secondprotocol (Step) as depicted in FIG. 19, to a subject in an experimentalstudy of OPM system performance.

FIG. 21 is a graph depicting a result of applying the second protocol(Step) as depicted in FIG. 19, to a high pain threshold subject in anexperimental study of OPM system performance.

FIG. 22 is a diagram illustrating a preferred embodiment of apain-monitored, closed-loop analgesia system employing an OPM system,such as is illustrated generally in FIG. 1.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

A preferred embodiment of a system and method for measuring pain in asubject is practiced using an objective pain measurement (OPM) system 5illustrated, by way of example, in FIG. 1. Preferably, the OPM system 5includes a signal acquisition subsystem 10, a signal processingsubsystem 11, and a communication link 8 between the two subsystems 10,11. The OPM system 5 may be implemented by separating the functionalelements for signal acquisition and signal processing if remote analysisof locally acquired pain signal data is desired. However, the OPM system5 is preferably implemented in one location. Thus, the communicationlink 8 between the signal acquisition subsystem 10 and the signalprocessing subsystem 11 may be internal to the processing architectureof the OPM system 5. For example, the communication link 8 may be anelectrical connection between two hardware components that respectivelyperform the functions of the two subsystems 10, 11. Generally,information is communicated over the communication link 8 from thesignal acquisition subsystem 10 to the signal processing system 11.However, the communication link 8 may also be bi-directional (as shownin FIG. 1) for certain applications, such as in a network-based system,as described hereinafter. Alternatively, the subsystems 10, 11 may beimplemented in software such that the link 8 is software implemented. Inanother alternate embodiment, the two subsystems 10, 11 lack acommunication link 8 using instead, for example, shared memory. If thesubsystems 10, 11 are in different locations, the communication link 8may be any convenient means for remote communication, including variousforms of wired and wireless communications.

FIG. 2 illustrates an example of a preferred embodiment of an OPM systemimplemented as an OPM network 95 where a centralized signal processingsubsystem 11 is networked to a plurality of signal acquisitionsubsystems 10. FIG. 2 depicts ten signal acquisition subsystems 10 witha single signal processing subsystem 11 as an example of one possiblenetwork configuration 95 of the OPM system 5. It is understood that theOPM network 95 is not inherently limited in terms of the number ofconnected subsystems 10, 11. Specifically, in the embodiment shown inFIG. 2, OPM data is collected at one location and evaluated at anotherlocation. The data may be provided from one of the signal acquisitionsubsystems 10 to the signal processing subsystem 11 by any availablemeans of communication. In one embodiment, the communications for theOPM network 95 are implemented using Internet Communication or switchedtelephone line services, such as using 56 Kbps modems or ISDNinterfaces. The OPM network 95 is also optionally adapted to high-speedaccess over available high-speed links, such as T1, T3, ADSL, telephonelines, cable modems or other means of high-speed access. Thecommunications are alternatively implemented using available wirelesscommunicating means, including, for example, satellite systems,terrestrial systems, or Blue Tooth. In an Internet communicationconfiguration, preferably the signal acquisition subsystems 10 securelycommunicate with the signal processing subsystem 11 via an Internetwebsite that preferably requires log-in and password entry. In apreferred embodiment, a mechanism is provided for Internet transmissionof collected data from the signal acquisition subsystems 10.Furthermore, the transmission mechanism may allow users associated withthe signal acquisition subsystems 10 to retrieve analyzed data,preferably in the form of pain measurement reports. The retrievalprocess preferably allows the users to have the reports securelydownloaded, e-mailed, faxed or otherwise sent to the signal acquisitionsubsystem 10, to a fax machine, or to any other data output computer orterminal that can display, produce, or otherwise output analysisreports. The retrieval process further allows the reports to be sent bytraditional mail. Optionally, an invoice for the service of generatingthe reports may be transmitted or otherwise sent to the user along withthe report.

The signal acquisition subsystem 10 may also be a portable device thatcollects pain signal data and stores the data for further signalprocessing at a later time. In one preferred embodiment, the signalacquisition subsystem 10 is conveniently carried by the subject duringnormal activity over a prescribed period (e.g., 24 hours). During thisperiod, the signal acquisition subsystem 10 collects and stores raw painmeasurement data. Then, at a convenient time, the data from the signalacquisition subsystem 10 is downloaded or otherwise transmitted to asignal processing subsystem 11 for analysis. The portable signalacquisition subsystem 10 may be similar to the Holter-type device usedfor cardiac applications. The download procedure may take place, forexample, in a doctor's office or hospital that the patient visits afterthe data acquisition period. Alternatively, the download procedure mayoccur remotely, such as via a distributed electronic network (e.g.,Internet). A physician or other care provider may then process the rawpain signal data from the signal acquisition subsystem 10 and obtain thepatient's pain profile for the signal collection period. Accordingly,the doctor or other care provider can then consider the pain profileresults with other observations in order to make a diagnosis andrecommend patient treatment.

In another embodiment, the OPM network 95 is wholly implemented in alocal area (such as within a clinic or hospital), as a local network(such as an Intranet, client-server system, or other similarly sizednetwork). In this aspect, the communications are preferably implementedusing local network systems and protocols such as Ethernet, TCP/IP,parallel port, serial port, and the like. A wireless data communicationsystem is optionally implemented, preferably using infrared, RF, or oneof the ISM (Industrial, Scientific and Medical) bands, or otherfrequencies.

In yet another embodiment, the OPM system 5 is a fully-integrated,compact device in which many of the functional elements may be renderedon an integrated circuit, such as an ASIC. In one such embodiment, theOPM system 5 may be conveniently integrated with other physiologicalsignal measurement devices, for use, for example, in a doctor's officeor in a hospital (e.g., emergency or operating room). An integrateddevice for measuring multiple physiological signals is conceptuallyillustrated in FIG. 3. FIG. 3 illustrates, by way of example, aphysiological monitoring system 100, including components for measuringblood pressure, pulse, temperature, respiration and pain level.Alternatively, a physiological monitoring system may include a componentfor objectively measuring pain level along with other monitoringcomponents. In a preferred embodiment, each of the measurements is takenusing an objective measurement instrument.

In another form of an OPM system 5, the OPM system 5 is implemented as afully integrated compact device, including a reusable sensor apparatusembedded within the device. The OPM system 5 may be in a form of acolor-coded strip. The OPM strip preferably acquires and processes thepain signal using analog circuitry, and preferably presents the painresult as a specific color scale or gray scale intensity. The OPM stripmay generally implement the OPM system 5 in a simplified form. Inaddition to (or in place of) the color or gray scale, the OPM strip mayprovide a discrete pain reading (similar to forehead thermometers),using, for example, a four category color-coded display (No Pain, MildPain, Moderate Pain, and Severe Pain).

The OPM strip preferably includes conductive sensors, analogamplification and filtering circuitry, analog processing circuitry, anda color display. The analog processing circuitry integrates the painsignal in the relevant frequency bands. Optionally, the OPM strip maypresume negligible motion artifacts.

To use the strip, the subject is preferably positioned (e.g., reclining)so as to remain stationary. This may help reduce signal artifacts causedby movement and may increase the accuracy of the pain reading. The OPMstrip is then firmly attached to the forehead of the subject. A periodof time is permitted to elapse while the subject remains stationary.After the prescribed period has elapsed, the strip may then be removedfrom the forehead of the subject and examined to determine the severityof the subject's pain.

FIG. 4 details preferred elements of the signal acquisition subsystem10, in the OPM system 5. The signal acquisition subsystem 10, 20preferably includes a set of electrodes 12 on a sensor array 13, whichoptionally may be in the form of a sensor strip, (various embodiments ofwhich are described herein). The signal acquisition subsystem 10, 20preferably further includes an amplifier 14, a band-pass filter 16 andan analog-to-digital (A/D) converter 18. Optionally, the signalacquisition subsystem 10, 20 further includes a component forelectrically isolating the subject, such as an optical isolator, and amemory (not shown) or other recording means for storing digitized datafor further processing. In an alternative embodiment, the signalacquisition subsystem 10, 20 lacks an analog-to-digital converter. Inthis embodiment, the acquired signal information may be stored on ananalog recording device, such as magnetic tape. The signal acquisitionsubsystem 10, 20 may also include a power source (not shown) thatsupplies the power to operate any array circuitry associated with orpart of the sensor array.

One example of a sensor array is depicted in FIG. 5 in the form of asensor strip 30. The electrodes 32, 34, 36 preferably are surfaceelectrodes that contact the subject's skin surface. More preferably, theelectrodes 32, 34, 36 each have a single contact and in combinationcomprise a left channel electrode 32, a right channel electrode 36 and areference electrode 34. Preferably, the array, is applied to thesubject's forehead such that left channel electrode 32 is positioned onthe left side of the subject's forehead, and right channel electrode 36is positioned on the right side of the subject's forehead so that itsubstantially mirrors the location of left channel electrode 32.Reference electrode 34 is preferably positioned midway between the leftchannel electrode 32 and right channel electrode 36, generally in themiddle of the subject's forehead. See FIG. 6. Preferably, signaldetection and measurement is performed at each electrode 32, 34, 36.

In general, electric potential changes (electrical activity) on thesubject's skin surface are generated by several sources, includingbackground electroencephalographic (EEG) activity, electrodermalactivity, electromyographic (EMG) activity, motion artifacts (such ascaused by eyeball, eyelid and head movements), and otherelectrophysiological phenomena. Referring to FIG. 5, the sensor array 30comprising the left and right channel electrodes 32, 36, enables the OPMsystem 5 to perform pain detection and pain quantification based on thesignals detected from the electrodes 32, 34, 36. Pain detection usesboth the left and right channel electrodes 32, 36 to distinguish painsignals from other signals in the relevant frequency range. Electrodes32, 36 are preferably placed on the subject's forehead, with the leftand right channel electrodes 32, 36 symmetrical about the subject'svertical midline. See FIG. 6. In general, background EEG measurementsfrom each side of the vertical midline are negatively-correlated. Otherartifacts, such as those caused by eyeball movement, are likewisenegatively correlated. In contrast, pain signals from each side of thevertical midline are generally positively correlated and may overridethe negatively correlated EEG activity. Consequently, pain detectionpreferably uses positive correlation as a discriminant for pain signalswhen the measurements are taken from electrodes located on oppositesides of the subject's vertical midline.

The pain detection may also use signal linearity to distinguish pain.This is because pain signals detected from each side of the verticalmidline are generally linearly related. In contrast, various artifactsin the detected signal, even those that are positively correlated (e.g.,eyelid or head movements), are often not linearly related; thus,artifacts may be distinguished from pain signals based on thisadditional discriminant.

In an alternate embodiment, electrodes 32, 36 are not on the subject'sforehead but remain substantially symmetrical with respect to thesubject's vertical midline. For example, the electrodes 32, 36 may beoptionally placed on the subject's scalp, symmetrical about the verticalmidline where it extends over the top of the subject's head. In thiscase, electrodes 32, 36 are still placed on either side of the verticalmidline. Hair on the subject's head may be removed to minimize signalinterference.

Pain quantification may use a single signal channel. In a preferredembodiment, an electrode used only to measure signals for painquantification is placed on the subject's head. However, any location onthe subject, such as an arm, leg, or torso, may also be used. In an OPMsystem 5 that performs pain quantification, the sensor array maycomprise a single signal electrode and a reference electrode.

Alternatively, two or more signal channels may be used for painquantification. In one such embodiment, the sensation of pain isseparately quantified for signals coming from each side of the verticalmidline, and the stronger of the two pain signals is used to measurepain intensity. It is desirable to use signals obtained from each sideof the vertical midline because pain originating on one side of thesubject (e.g., right hand, left foot, etc.) may have a contralateralrepresentation. That is, the pain may be perceived and/or detected bythe OPM system 5 primarily on the side of the vertical midline oppositethe pain's source. Beneficially, intensity measurements from multiplechannels may also be averaged, combined or otherwise used together toprovide a final pain intensity measurement. Multiple channels may alsobe used to determine where (e.g., left or right side) the subject's painsource is located.

Functionally, the electrodes 32, 34, 36 detect electrical activitychanges, i.e., voltage changes, between two contacts of each electrode.Typically, the detected magnitude of electrical activity is in the rangeof 0 to about 500 microvolts. In the embodiment depicted in FIG. 5, aground electrode 38 is separately provided from the sensor array 30(here in the form of a sensor strip) and positioned elsewhere on thesubject, such as an arm or leg.

The electrodes 32, 34, 36 are preferably circular, oval orellipse-shaped and positioned vertically relative to the sensor array30, when it is located on the subject's forehead. These shapes provideeach electrode with significant skin contact area while maintaining afunctionally desirable distance between each electrode 32, 34, 36.Alternative electrode shapes and configurations that correspond toequivalents known in the art may also be used.

The voltage levels detected by the electrodes 32, 34, 36 are transmittedto an amplifier 14, preferably located off of the sensor array 30. SeeFIG. 4. Alternatively, or in addition to external amplification, thesensor array 30 may include amplifier circuitry 14 that amplifies thesignals from electrodes 32, 34, 36 into a desired voltage range. In apreferred embodiment, the voltage range is between zero and about fivevolts.

As another option, the sensor array 30, 13 includes preamplifiers (notshown) proximal to electrodes 32, 34, 36 on the sensor array 30, 13.These preamplifiers perform initial signal amplification withoutamplifying subsequently acquired noise contributions. As another option,the sensor array 30, 13 includes dedicated active and/or passive filtersto remove electrophysiological artifacts, radio frequencies and otherelectromagnetic interference.

In yet another embodiment, the sensor array 30, 13 includes fiber opticconnections from electrodes 32, 34, 36 to amplifier 14. In thisembodiment, the sensor array 30, 13 includes transducers (not shown)that convert electrical signals from electrodes 32, 34, 36 to lightsignals. The light signals are then propagated through optical fibersand reconverted to electrical signals by a converter at the amplifier14. If the sensor array 30, 13 includes preamplifiers, the opticalfibers may also communicate the signal between the preamplifiers and theamplifier 14.

The use of optical fiber to communicate the signal to the amplifier 14reduces additional noise typically caused by signal transmission fromelectrodes 32, 34, 36. Furthermore, the optical fibers may provide thedesired optical isolation between the subject and the rest of the signalacquisition subsystem 10, 20.

Once the signals from electrodes 32, 34, 36 are amplified, the signalsfrom the left and right channels preferably pass through a filter 16 toremove contributions outside a frequency range, preferably about 0.1Hertz to about 5 Hertz. The applicants have found that within thisgeneral frequency range electrical signals corresponding to theintensity of the subjective experience of pain may exist forsubstantially all subjects. Furthermore, signals between about 0.5 Hertzand about 2 Hertz appear to carry the bulk of pain intensityinformation. As a result, the circuitry on the sensor array 30, 13 mayinclude capacative, inductive and/or resistive components to perform theband-pass filtering in the desired range, whether from about 0.1 Hertzto about 5 Hertz, from about 0.5 Hertz to about 2 Hertz, or anotherconvenient range within the more general range of about 0.1 Hertz toabout 5 Hertz. In this and other embodiments, filtering may take placebefore the signals are amplified.

In this embodiment, the signals are preferably received at the A/Dconverter 18, which digitizes the analog signals into discrete digitalsamples. See e.g., FIG. 4. The sampling of the signal at the A/Dconverter 18 should be greater than about 10 Hertz. But oversampling maybe used to improve accuracy, and so the preferred sampling rate is in arange around about 250 Hertz. In another embodiment, the filter 16 mayinclude a digital filter implemented on a signal processor after the A/Dconverter 18 has digitized the signal.

FIG. 6 depicts an alternative embodiment of a signal acquisitionsubsystem 10, 40 that generates digitized sample data for further signalprocessing. The signal acquisition subsystem 10, 40 preferably performssignal acquisition and signal conditioning before the digital signal isfurther processed for pain detection and quantification. Signalconditioning preferably uses a multi-channel variable gain and aspectral shaper (not shown). The conditioned signal is preferablyobtained with a 16 bit A/D converter, 48, at a sampling frequency ofpreferably about 250 Hertz.

In the embodiment depicted in FIG. 6, the signal acquisition subsystem10, 40 acquires electrophysiological signals from a subject's foreheadfor further processing and integration by the signal processingsubsystem 11 (not shown). The signal acquisition subsystem 10, 40includes multi-channel, signal-specific, differential amplifiers 46. Theamplifiers 46 are individually tuned according to signal type, rangingfrom medium-gain, very-low-frequency amplifiers (60 dB, 0.1-2 Hertz) foracquisition of slow central nervous system (sCNS) signals, to high-gain,medium-frequency amplifiers (80 dB, 2-100 Hertz) for acquisition offaster CNS (fCNS) signals. Preferably, the signals are acquired using asensor array 42 attached to the subject's forehead and designed foroptimal reception of the electrophysiological signals. The sCNS and fCNSsignals are then processed separately, quantified, and integrated toprovide a sensitive, specific, and accurate reading of pain level.

In another preferred embodiment, the signal acquisition subsystem 10, 40performs the steps of conditioning fCNS and/or sCNS signals (e.g., byamplification & spectral shaping), and digitizes the signals, mostpreferably using data sampling and storage.

Importantly, each of the sensor arrays described herein may include oneor more of the optional components discussed in connection with sensorarray 30, depicted in FIG. 5. Moreover, FIGS. 6, 7, 8 and 9 detailspecific preferred embodiments of a sensor array. For example, FIG. 7depicts a preferred sensor array embodiment 50, 42. The preferred sensorarray 50 includes left and right channel electrodes 56, 58, a commonelectrode 54 and a ground electrode 52 on the sensor array 50 above thearray's center point 60. Alternatively, the positions of the groundelectrode 52 and the common electrode 54 may be exchanged. Preferably,the ground electrode 52 is substantially shaped as ahorizontally-oriented oval or ellipse. Similarly, the common electrode54 is substantially shaped as a horizontally-oriented oval or ellipseand located below the center point 60 of the sensor array 50. Thehorizontal orientation of the ground and common electrodes 52, 54preferably preserves a functional distance between them while providingeach with a substantial contact area.

FIG. 8 depicts a detailed view of a second preferred embodiment of asensor array 24. This sensor array 59 includes electrodes 51, 53, 55, 57that correspond in function and general location to electrodes 52, 54,56, 58 illustrated in FIG. 7. However, sensor array 59 has preferreddimensions, that include the size, shape and location of the electrodes51, 53, 55, 57. In a most preferred embodiment, the electrodes aresubstantially elliptical and have a major axis length of about 15 mm anda minor axis length of about 4 mm. The particular size and shape of thesensor array 59 and the size, location, and shape of the electrodes isdesigned to maximize the signal to noise ratio in the relevant frequencyrange for a wide variety of patient forehead sizes and shapes. FIG. 8also discloses a tail 500 for combining the lines from each electrodeonto a single cable and terminating the lines at a connector 44. Thisfeature may be used with any sensor array.

As shown in FIG. 9, the sensor arrays described herein preferablycomprise several layers of bio-compatible material for detectingelectrical activity. FIG. 9 illustrates an exploded view of the layersof sensor array 30 of FIG. 5. The layers of the sensor array preferablyinclude an optional top shielding layer 550, a printed circuit layer 552beneath the optional top shielding layer 550, an articulated foam layer554 beneath the printed circuit layer 552, and, optionally, an adhesivelayer 556 beneath the articulated foam layer 554.

The shielding layer 550 is preferably comprised of a conductive materialformed as a grid (e.g., a silver ink grid) and provides shielding fromvarious potentially interfering sources that may reduce the availablesignal-to-noise ratio. The shielding layer 550 preferably shields theelectrodes 51, 53, 55, 57 from potential electrical, radio frequency andother electromagnetic interference, including, in particular, relativelyhigh frequency noise (greater than about 10 Hertz) modulated byrelatively low frequency noise (less than about 5 Hertz), which maycontaminate the pain signal.

The printed circuit layer 552 preferably comprises an insulating,non-conductive material, such as appropriate polymer-based plastics, asare well known in the art. Preferably, the printed circuit connectionsfor the sensor array are located on the material's underside. Theprinted circuit connections may comprise a conducting material forcarrying the electrical signal, such as silver ink. The circuitconnections extend from one edge of the printed circuit layer 552 to thelocations of the respective electrodes. At the electrodes' respectivelocations, the silver ink print (or other conductor print) isappropriately shaped (e.g., oval or elliptical) and forms the electrodecontact at the printed circuit layer 552. Preferably, at the location ofeach electrode, the print is coated with an electrical interfacematerial, such as silver chloride. Because a conducting gel initiallycarries the electrical signal from the subject's skin to the printedcircuit layer 552, the electrical interface material is preferably usedbetween the conducting gel and the circuit print to reduce any buildupof static charge and/or polarization effect occurring at the interfaceof the two materials.

The articulated foam layer 554 is comprised of any appropriateinsulating nonconductive material as is known in the art, and has holes558 that are preferably substantially shaped like and sized like theelectrodes 51, 53, 55, 57 at the electrodes' locations. The foam layer554 is preferably smooth and flexible so as to easily contour to thesubject's skin surface. Preferably, a conductive gel, preferablyhydrogel or wet gel, fills the holes 558 and extends to the next layer(e.g., the adhesive layer 556), if one exists.

The optional adhesive layer 556 also includes holes 560 that arepreferably shaped like and sized like the electrodes 51, 53, 55, 57 atthe electrodes' locations. Like the articulated foam layer 554, theadhesive layer 556 is preferably smooth and flexible so as to easilycontour to the subject's skin surface and so the layer remainsstationary once affixed on the subject's skin.

FIG. 10 illustrates an alternative configuration of a sensor array 502that has alternative dimensions, locations and sizes for the electrodes.The sensor array is preferably configured for use with a wet conductivegel.

Referring again to FIG. 1, in a preferred embodiment of the OPM system5, the output of the data acquisition subsystem 10 is transmitted to asignal processing subsystem 11. Contemporary forms of transmissioninclude Universal Serial Bus, PCMCIA card, PCI card, SCSI, FireWire, andBlue Tooth. FIG. 11 details one specific hardware embodiment of a signalprocessing subsystem 60, 11, such as that generally depicted in FIG. 1.

In FIG. 11, the signal processing subsystem 60; 11 preferably includes aprocessor 62 and a memory storage 64. The signal processing subsystem60, 11 further may include an output interface 66 such as a videodisplay and/or a speaker, and an input interface 68 such as one or moreknobs, a touch panel, a keyboard, a mouse and/or a microphone. Via theinput interface 68, a user may control the operation of the signalacquisition subsystem 10 by issuing commands, processed by the processor62, that begin and end digital data receipt and storage. The digitalelectronic activity signals received by the processor 62 are optionallydisplayed on the output interface 66 (e.g., video display) and stored inmemory storage 64. Preferably, the output interface 66 graphicallydisplays the signal processing results from the processor 62.

The signal processing subsystem 60, 11 may comprise a computer (likethose manufactured by IBM® or Apple®) with a monitor, such as a cathoderay tube (CRT) or liquid crystal display (LCD). Computer software may beused for the signal processing subsystem 60, 11 because softwareprovides flexibility in programming and modifying the software,displaying results, and running other peripheral applications.Alternatively, the signal processing subsystem 60, 11 may be implementedusing any type of processor or processors that analyze electricalactivity measurements as described herein. Thus, as used throughout, theterm “processor” refers to a wide variety of computational devices ormeans including, for example, using multiple processors that performdifferent processing tasks or having the same tasks distributed betweenprocessors. The processor(s) may be general purpose CPUs or specialpurpose processors, such as those often used in digital signalprocessing systems. Further, multiple processors may be implemented in aserver-client or other network configuration or as a pipeline array ofprocessors. Some or all of the processing may be alternativelyimplemented with hard-wired circuitry such as an ASIC, FPGA or otherlogic device. In conjunction with the term “processor,” the terms“memory” and “computer media storage” refer to any storage medium thatis accessible to a processor that meets the memory storage needs for theOPM system 5 or its components.

FIG. 12 illustrates a preferred embodiment of the functional elements ofa signal processing subsystem 60 previously represented in an exemplaryconfiguration hardware in FIG. 11. As depicted in FIG. 12, the signalprocessing subsystem 60 preferably includes a signal preparer 69, a painintensity quantifier 61, a pain detector 63, a confidence assessor 65and an output interface 67. As referenced above in the definition of a“processor,” the signal preparer 69, the pain intensity quantifier 61,the pain detector 63, and the confidence assessor 65 may be implementedin separate processors, in a single processor or in any other convenientconfiguration. The signal preparer 69 preferably normalizes the data toaccount for signal gain variations and noise. Preferably, the data isthen channeled into two processors, (most preferably operating inparallel), comprising the pain detector 63 and the pain intensityquantifier 61. The pain detector 63 determines whether pain exists inthe subject. The pain intensity quantifier 61 measures the level of painin the subject. In one embodiment, the pain intensity quantifier 61executes a primary algorithm or process for quantifying a pain levelfrom an extracted pain signal. Further, the pain detector 63 preferablyexecutes an auxilliary algorithm or process that uses complementary datato determine whether pain exists and to increase the pain readings'specificity and sensitivity. The output from each processor 61, 63 isinput to the confidence assessor 65, which then outputs the result tothe output interface 67. The output interface 67 in turn presents thedata to the OPM system user.

FIG. 13 illustrates one embodiment of a signal preparation method 70perfomed by a signal preparer, such as the signal preparer 69represented in FIG. 12. Preferably, the input to the process 70 is theglobal sample set of discrete signal values from a signal acquisitionsubsystem 10. In one step 72, the mean of the global sample set ofdiscrete sample values is determined. In another step 74, the calculatedmean is substracted from each of the sample values in the global sampleset. The resulting set of values represents a zero-mean sample set. Inanother step 76, a band-pass filter is optionally applied to the zeromean sample set. In yet another step 78, the filtered sample set isnormalized to account for variations in the acquired signal due to thephysical differences between subjects.

In another step 79, the normalized global sample set is divided intosegments preferably of a fixed size. Preferably, the particular size ofthe segments is determined to optimize for pain signal measurement. Thepreferred segment sizes are based on the pain signal's spectralcharacteristics, which affect the desired segment time. Most preferably,for a frequency range of about 0.1 Hertz to about 5 Hertz, the segmenttime should be between about four seconds and about 20 seconds. Thus,for example, using a preferred sampling rate of 250 Hertz, the preferredsize of each segment is in the range of about 4000 samples per segment.

The signal preparation method 70 may be mathematically expressed asfollows:

x=x(i)=y(i·ΔT)  (1)

Here, y is the continuous analog measurement, ΔT is the sampling period,and i is the sample number. The zero-mean sample set may then beexpressed as follows:

{tilde over (x)}(i)=x(i)−{tilde over (x)},  (2)

where {tilde over (x)} is the mean of x and {tilde over (x)}(i) has azero mean. In another step, a band pass filter is optionally applied:$\begin{matrix}{{x_{BPF}(i)} = {\sum\limits_{j = 1}^{Q}\quad {b_{j} \cdot {\overset{\sim}{x}\left( {i - j} \right)}}}} & (3)\end{matrix}$

where ${{\sum\limits_{j = 1}^{Q}\quad b_{j}} = 1},$

j is an index into the window of width Q of the band pass filter, b_(j)are band pass filter coefficients, and x_(BPF)(i) are filtered values ofthe signal.

In another step, the signal values are preferably normalized to accountfor differences in signal acquisition between subjects who have beengiven the same pain stimulus. Differences in the acquired signal may becaused by variations in the characteristics and/or quality of the signalpropagating media of the subjects such as, for example, differences inbone thickness, skin conductance/impedence, and otherelectrophysiological properties. These differences may be caused by thesubject's age, gender, dehydration, mood, and the like. Preferably, thesignal values are normalized so as to cancel out the effects of thesedifferences.

The signal values may be normalized in any number of ways. In oneembodiment, the signal values are divided by a normalizing term such asthe variance (or standard deviation) of the signal values. Thus,mathematically, the normalization may be expressed as: $\begin{matrix}{{x_{N}(i)} = \frac{x_{BPF}(i)}{\sigma_{x}}} & (4)\end{matrix}$

where σ_(x) is the standard deviation of x_(BPF)(i), and x_(N)(i) arenormalized values for x(i). Alternatively, a variance (or standarddeviation) term may be determined based on samples taken outside of therelevant pain frequency range to avoid any contribution by the painsignal itself to the normalizing term. In other embodiments, thenormalization step may be based on an assessment of one or more physicalcharacteristics of the subject that relate to the variations in theacquired signal.

Preferably, in another step, the global sample is divided into Msegments, each having L samples, where k is a sample index in eachsegment, U is the number of samples between segments and p is thesegment number. Thus,

x _(N,p)(p·U+κ)=x _(N)(i)  (5)

where 1<k<L, 0<p<M. As may be seen from Equation (5), where U<L, the Msegments in the global sample set may partially overlap each other.

In another embodiment, the acquired signal from the signal acquisitionsubsystem 10 is preprocessed for rejection of motion and other artifactsand then normalized to reduce inter- and intra-subject signalvariability. In this embodiment, the signal preparation (preprocessing)steps generally include: a segmentation step, preferably by grouping apredetermined number of samples to form a data segment; a meansubtraction step, preferably by subtracting the mean value sample bysample from each data segment; and a normalization step of each datasegment, preferably by signal variance. Optionally, the segmentationstep may be performed after the mean substraction step and thenormalization step. In this case, the mean and variance values are basedon the global data set.

Signal conditioning, acquisition and preparation (preprocessing) may beaccomplished, for example, by using the following mathematical steps:

Digitization: In this step, amplitude measurements (e.g., in microvolts)are preferably taken from either of the preferably two availablechannels (left or right electrode) and digitized. This step may berepresented by the equation (Eqn. 6):

x ¹(n)=x ⁰(n·ΔT),  (6)

such that ΔT=1/F_(s) where ΔT is the sampling period (e.g., 4 msec) andF_(s) is the sampling frequency (e.g., 250 Hertz). In the aboveequation, n is the sample number, x⁰ are the measured analog values, andx¹ are the discrete sample values.

Segmentation: In this step, the signal values are parsed into segmentsof L samples, where a new segment begins with the first sample followingthe last sample in the previous segment. Thus, sets of segmentspreferably are defined by:

x _(k,L) ¹(n)=x ¹(k·L+n),  (7)

where 1000<L<5000,k=1,2, . . . , K, there are K segments and k is thesegment number.

Mean subtraction: In this step, the mean within a segment is subtractedfrom the value of each sample in the segment. This step is preferablyperformed for all segments in the data set. Thus, this step may beexpressed as:

x _(k) ³(n)=x _(k) ²(n)−AVG{x _(k) ²(n)}  (8)

Normalization: In this step, the signal values are normalized. In oneembodiment, the normalization step is performed by dividing each sampleby the variance of the sample set, preferably the global sample set, butoptionally the sample set for each segment. Thus, this step may berepresented by: $\begin{matrix}{{x_{k}^{4}(n)} = \frac{x_{k}^{3}(n)}{{VAR}\left\{ {x_{k}^{3}(n)} \right\}}} & (9)\end{matrix}$

A preferred embodiment of a method of performing pain detection, such asperformed by the pain detector represented in FIG. 12, makes use of twoprocessed signals. Preferably, the normalized and segmented data sampleset serves as an input to the pain detection process (such as thatoutput from FIG. 7B's signal preparer 69. As discussed above, the paindetection method determines whether the signals from the left and rightchannels are positively correlated and whether the signals from the twochannels are linearly related in the relevant frequency band. In onepreferred embodiment, the correlation coefficient, ρ, is used todetermine whether the signals are positively correlated. The correlationcoefficient, ρ, is preferably determined by determining the covarianceof the signals from the two channels, according to the following:$\begin{matrix}{\rho = \frac{{COV}\left\lbrack {{x_{i}(n)},{y_{i}(n)}} \right\rbrack}{\sqrt{{{VAR}\left\lbrack {x_{i}(n)} \right\rbrack} \cdot {{VAR}\left\lbrack {y_{i}(n)} \right\rbrack}}}} & (10)\end{matrix}$

where y_(i)(n) are the normalized signal values in a segment from theright channel, x_(i)(n) are the normalized signal values in the segmentfrom the left channel, VAR[x_(i)(n)] and VAR [y_(i)(n)] are thevariances for the respective sets of signal values, and COV [x_(i)(n),y_(i)(n)] is the covariance between the two channels, meaning that:

COV [x _(i)(n), y _(i)(n)]=E{(x _(i)(n)−η_(x))(y _(i)(n)−η_(y))}  (1)

where η_(x) and η_(y) are the means of x_(i)(n) and y_(i)(n)respectively. Generally, the correlation coefficient, ρ, has valuesbetween −1 and 1.

Similarly, in a preferred embodiment, a coherence, C_(xy)(ω), of asegment from the left and right channels is evaluated to determinewhether the signals are linearly related. This embodiment may determinelinearity using the following equation: $\begin{matrix}{{C_{xy}(\omega)} = \frac{{{P_{xy}(\omega)}}^{2}}{{P_{xx}(\omega)}{P_{yy}(\omega)}}} & (12)\end{matrix}$

where ω is the frequency in radians based on a sampling frequency F ofpreferably about 250 Hertz, P_(xy)(ω) is the cross power spectrumbetween the two channels, P_(xx)(ω) and P_(yy)(ω) are the power spectrafor the respective channels, preferably measured between about 0.1 andabout 5 Hertz, and 0≦C_(xy)(ω)≦1. Generally, C_(xy)(ω) increases as asubjective perception of pain increases. Conversely, for low values ofC_(xy)(ω), no pain is generally experienced.

In an alternative embodiment, the pain detection method may include oneor more of the following steps:

(a) Band-pass filtering of left and right composite fCNS-sCNS data in arange of 0.1-30 Hertz, and preferably of sCNS data in a range of 0.1-5Hertz.

(b) Selection of consecutive data segments of left and right channels.

(c) Correlation analysis of the bilateral data segments.

(d) Derivation of confidence coefficient(s) from the correlationanalysis.

(e) Return to step (b) until end of recorded data.

In this alternative embodiment, the detection method preferably usescorrelation analysis of multichannel fCNS and/or sCNS signals toincrease sensitivity (i.e., produce lower false negative rates) andspecificity (i.e., produce lower false positive rates) in the pain levelreadings. Normally, the bilateral fCNS recordings that reflect cerebralactivity are negatively correlated, since the recordings are referencedto a center-located common lead. However, during pain periods, sCNSactivity may generate correlated surface activity that overrides theanticorrelated cerebral activity. The transition from negative topositive correlation may be detected before onset of a substantial painsignal, and thus its detection increases the sensitivity performance ofthe detection algorithm in the OPM system 5. Moreover, certain artifactsusually do not cause correlated bilateral activity, and thus the OPMsystem's specificity performance may be enhanced by rejecting thoseartifacts using the correlation analysis. Thus, in one embodiment, ifthe signal is positively correlated, it is classified as containing apain signal that may be evaluated to determine its magnitude. The painsignal's magnitude may be evaluated using the following calculations. Asdiscussed previously (see Equation (10)), where x_(i)(n), y_(i)(n) areconcurrent bilateral data segments, the correlation coefficient, ρ, forthe two signals is defined as: $\begin{matrix}{\rho = \frac{{COV}\left\lbrack {{x_{i}(n)},{y_{i}(n)}} \right\rbrack}{\sqrt{{{VAR}\left\lbrack {x_{i}(n)} \right\rbrack} \cdot {{VAR}\left\lbrack {y_{i}(n)} \right\rbrack}}}} & (13)\end{matrix}$

where |ρ|≦1

The correlation coefficient, ρ, is preferably used for confidenceanalysis of the corresponding pain level reading. A confidencecoefficient, η_(cor), is preferably defined as follows: $\begin{matrix}{\eta_{cor} = {\frac{1}{2} \cdot \left\lbrack {{{{sign}(\rho)} \cdot \sqrt{\rho }} + 1} \right\rbrack}} & (14)\end{matrix}$

to obtain a range of coefficient values between 0 and 1. Alternatively,to obtain a range of values for η_(cor) between 0 and 10, a multiplierof 5 (instead of ½), is used. The square-root operator is used so as toenhance the separation of values from the zero-midpoint for thecorrelation coefficient, or from the 0.5 or 5 midpoint for theconfidence coefficient, depending on the multiplier that is used.

The confidence coefficient, η_(cor), contributes to the system in atleast two ways. First, it provides increased sensitivity because theshift from negative to positive correlation is evident, even with smallmagnitude pain signals, and thus it can help detect low pain levels.Second, the confidence coefficient helps validate the pain reading,thereby enabling discrimination between true pain signals and artifactsthat generally do not elicit the negative-to-positive shift ofcorrelation.

The confidence coefficient may be defined so that it varies from 0(perfect negative correlation) to 10 (perfect positive correlation),thereby making it easier to visually display (and read) a confidencecoefficient diagram, optionally displayed with the primary pain reading.

In an embodiment of a pain detection method where channel correlationand channel coherence are evaluated (e.g., the first detectionembodiment described above), two confidence coefficients, i.e., theconfidence coefficient, η_(cor), from the correlation coefficient, ρ,and a confidence coefficient η_(coh), based on the coherencedetermination, may be evaluated and factored into the final pain result.Similar to Equation (14), η_(coh) may be expressed as:

η_(coh)=½[sign(2C _(xy)(ω)−1)·√{square root over (|2 C _(xy)(ω)−1|)}+1]

where η_(coh) has a range of values between 0 and 1. As with thecorrelation confidence coefficient, η_(cor), in Equation (14), η_(coh),may be rescaled to values between 0 and 10. The confidence coefficient,η_(coh) may also be applied in a similar way to modify the pain result.

FIG. 14 illustrates one embodiment of a pain intensity quantificationmethod 80, such as may be performed by the pain intensity quantifierrepresented in FIG. 12. In one step 82, the normalized and segmentedsample set is transformed into the frequency domain for each segment.

For example, for each segment, the discrete signal is transformed intothe frequency domain to obtain the power spectrum for each segment. Thismay be done using any of a number of methods including directly takingthe discrete Fourier transform (DFT) or using linear prediction coding(LPC). LPC is preferably used to perform the transformation for short,non-stationary data segments, such as are normally obtained from thepreprocessed pain signal. The following LPC equation is preferably used:$\begin{matrix}{{x(i)} = {{\sum\limits_{l = 1}^{s}\quad {a_{l} \cdot {x\left( {i - l} \right)}}} + {e(i)}}} & (15)\end{matrix}$

where S is the model order and has an integer value preferably in therange of 3 to 10 (e.g., 5), l is an index for the linear description,a_(l) represents the linear prediction coefficients, and e(i) is theerror term in the prediction. In a next step 84, a raw pain signal, R,between frequencies of interest are evaluated for each segment. Inanother step 86, the raw pain signal is scaled.

In one specific embodiment, the quantified pain signal preferably isextracted to provide a pain level reading using one or more of thefollowing steps:

(a) Band-pass filtering of sCNS data in a range of 0.1-2.0 Hertz;

(b) Selection of consecutive data segment;

(c) Linear prediction of the band-passed signal segment usingleast-square fitting;

(d) Transformation of the linear prediction parameters into frequencydomain;

(e) Non-linear weighted averaging of frequency domain signal;

(f) Taking the logarithm of the weighted average value;

(g) Scaling of logarithm result by calibration coefficients, yieldingpain level reading;

(h) Return to (b) until end of recording.

In the above sequence of steps, steps (b) through (e) generally measurethe power spectrum for the signal in the relevant frequency range. Steps(f) and (g) generally calibrate and scale the result to obtain a finalpain reading. The above sequence of steps may be implemented as follows:

Band-pass filtering (BPF) (a) (0.1-2.0 Hertz): In this optional step,the signal values are preferably filtered so that only effects withinthe range of about 0.1 to about 2.0 Hertz are determined. This step isrepresented by the following equation: $\begin{matrix}{{{x_{k}^{5}(n)} = {\sum\limits_{i = 1}^{Q}\quad {b_{i} \cdot {x_{k}^{4}\left( {n - i} \right)}}}},} & (16)\end{matrix}$

In Equation (16) above, b_(i) are the BPF coefficients, where i is anindex into the BPF window and Q is its size.

Power spectrum measurement (b)-(e): In this set of steps, the strengthof the signal is evaluated. In one embodiment, the strength may bedetermined by calculating the signal's power spectrum in the frequencyrange of interest. To do this, the signal preferably is represented inthe frequency domain, which may be achieved using linear predictionanalysis. Thus, continuing from equation (16), x(n)≡x_(k) ⁵ (n) is firstdefined. Generally, the linear prediction formula for x(n) is asfollows: $\begin{matrix}{{x(n)} = {{\sum\limits_{i = 1}^{P}{a_{i} \cdot {x\left( {n - i} \right)}}} + {e(n)}}} & (17)\end{matrix}$

where a_(i) are the linear prediction coefficients, e(n) is the errorterm in the prediction, n=P+1, . . . , N, and P is the model order.Transforming the linear prediction formula to matrix notation:

[x(n)]_((N−P)x1) =[x(n−i)]_(N−P)xP) ·[a _(i)]_(Px1)+[e(n)]_((N−P)x1)  (18)

where ${x\left( {n - i} \right)} = {\begin{bmatrix}{x(P)} & {x\left( {P - 1} \right)} & \cdots & {x(1)} \\{x\left( {P + 1} \right)} & {x(P)} & \cdots & {x(2)} \\{x\left( {P + 2} \right)} & {x\left( {P + 1} \right)} & \cdots & {x(3)} \\\vdots & \quad & \quad & \quad \\{x\left( {N - 1} \right)} & {x\left( {N - 2} \right)} & \cdots & {x\left( {N - P} \right)}\end{bmatrix} \equiv \underset{\_}{\underset{\_}{X}}}$ and${x(n)} = {{\begin{bmatrix}{x\left( {P + 1} \right)} \\{x\left( {P + 2} \right)} \\\vdots \\{x(N)}\end{bmatrix} \equiv {x\quad a_{i}}} = {{\begin{bmatrix}a_{1} \\a_{2} \\\vdots \\a_{p}\end{bmatrix} \equiv {a\quad {e(n)}}} = \begin{bmatrix}{e\left( {P + 1} \right)} \\{e\left( {P + 2} \right)} \\\vdots \\{e(N)}\end{bmatrix}}}$

The matrix notation of the linear prediction formula is:

x=X·a+e   (19)

and the least-square solution of the linear prediction formula is:

â =−( X^(T) · X )⁻¹· X^(T) · x   (20)

where â is the estimate for the linear prediction coefficients. Thelinear prediction coefficients are then transformed to frequency domainto obtain the power spectrum signal: $\begin{matrix}{{P_{xx}(w)} = {\frac{\sigma_{e}^{2}}{{{1 + {\sum\limits_{k = 1}^{P}{a_{k} \cdot ^{- {jwk}}}}}}^{2}} \sim \frac{1}{{{1 + {\sum\limits_{k = 1}^{P}{a_{k} \cdot ^{- {jwk}}}}}}^{2}}}} & (21)\end{matrix}$

where w is the frequency in radians, P_(xx)(w) is the power spectrum fora segment, and σ_(e) ² is the variance in the prediction error.Preferably, as one measure of the signal strength, the frequency domainsquared signal is integrated over the frequency range of interest:$\begin{matrix}{{R = {\sum\limits_{w = w_{1}}^{w_{2}}{P_{xx}^{2}(w)}}},} & (22)\end{matrix}$

where R is the raw pain reading and w₁, w₂ are frequency summationlimits.

Scaling (f)-(g): Having a representation of signal strength, R,additional steps preferably are performed to calibrate and scale R toyield the pain level result for each segment. In one preferredembodiment, the calibration and scaling of R is according to theequation:

PAIN=C ₁·log(R+C ₂);  (23)

where PAIN is the scaled and calibrated pain level, and C₁,C₂ arecalibration coefficients. Given an empirically determined range ofvalues for R, C₁ and C₂ are chosen so that PAIN ranges from 0 to 10.

Preferably, a setting of C₂=1 is selected so as to ensure that thenumerical value for PAIN is guaranteed to be positive. The log termcompresses the range of values onto a logarithmic scale, generallyrepresenting the relationship between the raw pain signal and thesubjective experience of pain. Other biological sensors, such as the earand the eye, similarly operate on a logarithmic basis. Furthermore, asetting of C₁=10.0 is preferably a value determined according toempirical studies to define the value of PAIN=1 as the threshold betweena subjective experience of pain and a subjective absence of pain for apredetermined percentage of normal subjects in a baseline test.(Examples of such empirical studies are discussed more fullyhereinafter.) These empirical studies demonstrate that a setting ofC₁=10 preferably establishes a threshold of 1.0 between pain and no-painsuch that at least about 99% of subjects experience a PAIN value≦1.0 inthe absence of a painful stimulus. Alternatively, C₁ may be set toanother value to establish a threshold of PAIN=1.0 such that some otherpercentage of subjects experience a value of PAIN≦1.0 in the absence ofa painful stimulus.

In other embodiments, C₁ may be a non-scalar term, such as a linear,quadratic, other polynomial or another function type to scale the rawpain signal according to other convenient or desireable criteria. Forexample, C₁ may be a quadratic or other function so as to produce a painresult that is calibrated to be in general accordance with theconventional VAS scale. As another example, C₁ and/or Equation (23) ingeneral may be defined to obtain a particular balance betweenspecificity (i.e., the extent to which affirmative readings of pain bythe OPM system 5 are correct) versus sensitivity (i.e., the extent towhich the OPM system 5 detects actual experiences of pain). In general,the OPM system has a range of performances that vary in terms ofspecificity and sensitivity. The C₁ term may be set to a particularperformance within the range based on predetermined relative prioritiesof specificity versus sensitivity.

Optionally, the final pain reading, PAIN, is modified by the results ofthe generation of the confidence coefficients, η_(cor) and η_(coh). Inother embodiments, the confidence coefficients may be used to generate aseparate value such as a number between 0 and 100, that represents apercentage of confidence in the quantified pain result. Generation andapplication of confidence values, such as η_(cor), and η_(coh), arepreferably perfomed in a processor, such as the confidence assessor 65depicted in FIG. 12.

One example of an approach to integrating the PAIN value, η_(cor), andη_(coh), where η_(cor), and η_(cor) have been scaled to values between 0and 10, may be expressed logically as follows:

if (PAIN<1),

then PAIN=PAIN.

else

If (η_(cor)<3) AND (η_(coh)<3),

then (PAIN=0).

else if (η_(cor)+η_(coh))<10,

then (PAIN=PAIN/(11−(η_(cor)+η_(coh)))).

The above logic incorporates several considerations. First, where thequanitifed PAIN value is very low (e.g., PAIN<1), then the confidencecoefficients do not affect the determination. The subject is consideredto have no experience of pain. Next, where the PAINvalue indicatessomething other than no pain (e.g., PAIN≧1), then where both confidencecoefficients have low values, the quantified pain signal is consideredto represent sources other than an actual experience of pain, andtherefore PAIN is set to zero. Where both confidence coefficients arenot considered too low to establish that no pain is present, then thePAIN reading is reduced based on the levels of the confidencecoefficients. Generally, the lower the values for the confidencecoefficients, the more the quantifed PAIN result is reduced. Finally,where both coefficients have relatively high values, the PAIN result(which should indicate that pain is present in the subject) is notmodified.

In the above example, the two confidence coefficients are given the sameweighted effect on the PAIN value. In other embodiments, the confidencecoefficients may affect the final PAIN result differently. Furthermore,it can be seen that the confidence coefficients may be applied in avariety of other ways to produce the output PAIN result within acalibrated range.

An OPM system 5 has been extensively tested in several distinct studies.In a first study, an OPM system 5 was tested using a controlledQuantitative Sensory Testing (QST) protocol that employed a MedocTSA2001 QST device (Medoc Ltd., Ramat Yishai, Israel). The protocolincluded application of a gradually increased heat stimulus to asubject's palm. The temperature stimuli ranged from 32 to 48° C. Thesubject was instructed to report a subjective pain level using Medoc'sComputerized VAS (CoVAS) throughout the experimental session.

FIGS. 15 and 16 are graphs presenting heat stimulus intensity in dashedline. The temperature in celsius is divided by 10 in order to fit thepain scale. The patient's subjective evaluation is represented by adotted line, the objective pain level measurement provided by the OPMsystem 5 is represented by an asterisk-dashed line, and the confidencecurve, based on η_(cor) only, is represented by a circle-solid line.FIG. 15 depicts a first example of a typical result of objective painlevel monitoring versus subjective report. This example demonstrates thehigh correlation between the subjective report and the objectivereading. FIG. 16 depicts a second example that clarifies the benefit ofusing the confidence information for rejection of artifactual painreadings. The confidence curve provides a clear indication that theinitial pain reading is artifactual due to movement artifacts (note thelow confidence), while the second pain interval is indeed indicative ofpain (note high confidence).

In a second study, the OPM system 5 was tested in a clinical setting onsubjects in child labor. FIG. 17 illustrates an objective pain levelbetween contractions (asterisk-solid), and the progression of painduring a uterine contraction (solid), starting with baseline level,rising to peak value at contraction climax, and returning to baselinelevel when the subject relaxed.

In a third study, a subject population of healthy male and female adultvolunteers, each with uneventful past medical histories, was gathered.Specifically, the subjects were considered to be free of any skinabnormality or eruption (either recent or chronic), and to be unexposedto drugs or medications that affect the central nervous system.

The Quantitative Sensory Testing (QST) model was used for the painstudy. Using the Medoc TSA 2001 device, pain was induced by applying ametal plate to the skin (at the thenar emminence of the palm) of eachsubject. The plate was heated according to a predetermined protocol,such that pain of different levels was experienced. It is wellestablished that beyond a certain threshold (for most subjects between44° C. and 45° C.), the sensation of heat turns into a sensation ofpain. The testing system allowed the subjects to report their subjectiveexperience of pain magnitude by using a computerized visual analog scale(CoVAS) that was then synchronized with the signal acquisition subsystem10 such that the temporal correlation between subjective and objectivereports could be assessed.

Two protocols were designed for Quantitative Sensory Testing with theMedoc TSA2001 unit. The first was a Baseline-Pain protocol including abaseline period of 60 seconds (32° C.) followed by a painful period ofanother 60 seconds (48.3° C.), as shown in FIG. 18. The second protocolwas a Step protocol in which the subject was exposed for 60 seconds to abaseline stimulus (32° C.) followed by three 30-second cycles ofincreasing heat levels, namely 47.5° C., 48.0° C. and 48.5° C. andconcluded with a 60 second return-to-baseline period, as depicted inFIG. 19.

The Baseline-Pain protocol was applied to 55 healthy subjects. The Stepprotocol was applied to over 100 healthy subjects, nearly evenly dividedbetween men and women, and all aged between 18-55 years.

The testing setup included a computer-controlled TSA2001 device, aComputerized Visual Analog Scale (CoVAS), and a second computer attachedto amplification hardware (NORAV 1200S) for data acquisition andstorage. The stored data included three streams of data, namely (a) acontinuous record of the subjective CoVAS readings, (b) a continuousrecord of the applied heat intensities, and (c) a continuous record ofthe bi-channel amplified bio signals.

Testing was performed according to the following procedure:

1. All subjects received a detailed explanation of the test protocol andsigned an informed consent form;

2. The subjects were seated comfortably for at least five minutes beforestart of the protocol;

3. The forehead sensor array was applied, the system started and signalquality was assured;

4. The subjects were asked to confirm their state of comfort and toevaluate their stress level;

5. The metal plate of the Medoc TSA 2001 was firmly attached to theright thenar eminnence;

6. The subject was instructed how to use the CoVAS pain reportingsystem;

7. The experimental pain protocol was activated, and the CoVAS painreported;

8. Immediately after completion of the protocol, each subject wasadditionally asked to describe the pain on a numerical pain scale (NPS),a categorical pain scale (mild, moderate, severe) and to draw a mark ona 10 cm analog scale (VAS). In addition, subjects were asked to describethe level of stress experienced during the test; and

9. If at any time a subject expressed severe discomfort, the subject wasallowed to discontinue the procedure.

Generally, three classes of responses were obtained: Class I, comprisingresponses presenting high correlation between the objective reading andthe subjective report (˜60%); Class II, comprising responses that werenot significantly correlated with the subjective report (˜30%); andClass III, comprising “non-responders” (˜10%). The distinction betweenthe three types of responses is evident in the dynamic pain profiles,while average analysis for pain “spot-check” combines Classes I & IIyielding a total performance approaching 90%.

The results of the “Baseline-Pain” statistical protocol were evaluatedusing an averaged “spot-check” reading, in order to provide a mechanismfor a unidimensional, simple performance analysis. A reading of 0.7 orlower indicates no pain, 0.7-1.3 represents an inconclusive region, anda reading above 1.3 is considered as a true pain reading. The followingtable summarizes the statistical results obtained using the“Baseline-Pain” protocol. The inconclusive column represents borderlinepain readings.

Pain True False True In- CoVAS Gauge Posi- Nega- Nega- False con-Average Average tive tive tive Positive clusive Base- 0 0.44 — — 92% 6%2% line Pain 5.12 4.79 88% 8% — — 4%

Typical results of applying the Step protocol are depicted in the graphsof FIGS. 20 and 21. FIG. 20 presents high temporal correlation betweenthe subjective report (CoVAS) and the objective pain curve, detectingeven the very mild pain sensation of ˜1 during exposure to the firstheat cycle. FIG. 21 presents another case where a very mild pain cycleis not detected due to the is high pain threshold of the subject.

Statistical analysis revealed significant success rates of paindetection. The Step protocol yielded substantial correlation between thesubjective and objective pain curves, exhibiting fine-detailedidentification of even minute changes of the pain sensation reflected inthe subjective pain curves. These initial findings, although obtainedfrom small-scale studies, indicate that the OPM system 5 performsobjective detection and quantification of pain sensation.

Thus, one specific method of processing a CNS-generated composite signalto measure a subject's pain level may include any subset of thefollowing steps:

(a) Application of a unique sensor array to the subject's forehead;

(b) Acquisition and digitization of bioelectrical signals via a sensorarray;

(c) Separation of the bioelectrical signals to fCNS and sCNS components;

(d) Linear prediction analysis of the sCNS signals;

(e) Transformation of the prediction coefficients of the sCNS signals tofrequency domain signals;

(f) Quantitative analysis and scaling of the frequency domain signalsyielding an initial pain level score;

(g) Correlation and/or coherence analysis of composite fCNS-sCNSsignals;

(h) Derivation of correlation and/or coherence coefficients from thecorrelation analysis;

(i) Transformation of the correlation and/or coherence coefficient(s) toconfidence coefficient(s); and

(j) Integration of the initial pain level score and the confidencecoefficient(s) into a final pain level reading.

In another application of the OPM system 5, the pain monitoring methods,apparatuses and systems as described herein may be used to provideclosed-loop analgesia applications to patients. In one embodiment, asystem and method are provided for an individualized and automateddelivery of a patient's pain medication (i.e., a closed-loop analgesiasystem) that comprises objective CNS-based pain monitoring methods andapparatuses that use bio-electric signals.

In a separate embodiment, a drug delivery apparatus is integrated withan OPM system 5 that monitors pain levels (such as thorough CNSbiosignal measurement) in order to control the amount of analgesia orother medication administered to a patient. The closed-loop analgesiasystem may automatically monitor the pain level and/or automaticallyadjust the amount of medication delivered to the patient.

Such a closed-loop analgesia/pain monitoring system may use any numberof pain medication delivery methods, including intravenous delivery,epidural delivery, parenteral delivery, intramuscular delivery,intra-articular delivery (e.g. during surgery) and nasal delivery. Themedication delivery methods may employ delivery devices and deliverycontrollers suitable to their respective methods of delivery. The systemmay also employ a variety of pain medications, alone or in efficaciouscombinations, including morphine, buprenorphine, piritramide,remifenanil, and local anesthetics, or neurostimulation devices, such asTENS.

By way of example, FIG. 22 shows a pain-monitored, closed-loop analgesiasystem 140 comprising an intravenous (IV) fluid container 141, an IVdrop counter 142, an intravenous infusion pump 144 for a deliverydevice, a pump controller for a delivery controller 146, and an OPMsystem 148. The IV fluid container 141 is connected by a tube to the IVdrop counter 142, which measures the delivery of medication. The dropcounter 142 is similarly connected by a tube to the infusion pump 144,which delivers the medication to the patient. The drop counter 142 alsocommunicates with the delivery controller, preferably through anelectrical connection and preferably frequently providing the drop countto the delivery controller 146. The infusion pump provides the IVmedication and, along with the OPM system 148, is connected to thepatient. The OPM system 148 also communicates with the deliverycontroller 146, preferably via an electrical connection.

In order to regulate patient pain, the controller 146 automaticallyadjusts the medication amount by controlling the infusion pump 144 basedon changes in patient pain level (as measured by the pain measuringdevice). As with most drug delivery devices, the infusion pump 144 mayprovide either continuous or periodic medication as desired. Themedication may be provided in the form of boluses.

In other embodiments, a system for closed-loop pain controlled analgesiamay include any combination or subset of the following: (a) an OPMsystem; (b) an IV fluid container; (c) an IV drop counter; (d) aninfusion pump (a type of delivery device); and (e) a pump controller (atype of delivery controller).

In a preferred embodiment, the pain-monitored, closed-loop analgesiasystem 140 employs a method and apparatus for bio-signal monitoring,optionally including CNS-signal monitoring, wherein a pump controller isconnected in a signal-feedback loop with a pain measuring device, aninfusion pump, and a patient. However, other forms of controllers may beused, along with other drug delivery methods and mechanisms. In apreferred embodiment, the system includes a pain measuring device thatmeasures CNS signals from a patient's forehead, such as through the useof electrodes. The signals are then used in a signal feed back loop tocontrol a pain medication delivery device through the use of acontroller.

In another preferred embodiment, the system may employ an override fornausea, respiratory depression, hypoxia, and dizziness, whereby thepatient or caregiver can moderate the dose of pain medication based onthese indications. Furthermore, the system can automatically monitor thepatient for these override factors, including hypoxia and respiratorydepression. Optionally, the system may also automatically adjust thepatient's dosage of pain medication based on the detection of anoverride factor.

According to yet another preferred embodiment, a method of objectivepain monitoring is based on central nervous system signal analysis. Theanalysis is carried out on short data segments reducing the effects ofsignal nonstationarities. The proposed algorithms provide a window intothe central nervous system where different kinds of pain sensations maybe evaluated and monitored. The data resulting from the algorithm maythen be used to control the delivery of a pain medication to thepatient.

In another preferred embodiment, an apparatus enables replacement of thepatient's role in actively controlling the amount of administeredmedication by automatically detecting the patient's pain level andautomatically delivering the appropriate amount of analgesic.

While preferred embodiments of the invention have been described herein,and are further explained in the accompanying materials, many variationsare possible which remain within the concept and scope of the invention.Such variations would become clear to one of ordinary skill in the artafter inspection of the specification and the drawings. The inventiontherefore is not to be restricted except within the spirit and scope ofany appended claims.

What is claimed is:
 1. A system for acquiring a signal representative of a subjective perception of pain by a subject comprising: (a) a sensor array for measuring an electrical signal at a site on the subject; (b) an amplifier for amplifying the signal; (c) a band-pass filter connected to the amplifier for at least partially removing components of the signal below about 0.1 Hertz and above about 5 Hertz; and (d) an analog-to-digital converter for converting the signal into a set of discrete values.
 2. The system of claim 1, wherein the sensor array is connected to the band-pass filter, and the analog-to-digital converter is connected to the amplifier.
 3. The system of claim 1, wherein the sensor array is connected to the amplifier, and the analog-to-digital converter is connected to the band-pass filter.
 4. The system of claim 3, further comprising an optical isolator connecting the sensor array and the amplifier.
 5. The system of claim 4, further comprising a memory connected to the analog-to-digital converter for storing the set of discrete values.
 6. The system of claim 1, further comprising a memory for storing the set of discrete values.
 7. A method of acquiring an electrical signal representative of a subjective perception of pain by a subject comprising the steps of: (a) detecting an electrical signal at a site on the subject; (b) amplifying the signal; and (c) filtering the signal to at least partially remove components of the signal below about 0.1 Hertz and above about 5 Hertz.
 8. The method of claim 7 further comprising a step of converting the signal into a set of discrete values.
 9. The method of claim 6 further comprising the step of storing the set of discrete values.
 10. The method of claim 7 further comprising the step of recording the signal.
 11. An electrical signal containing information objectively describing an intensity of a subjective experience of pain in a subject obtained by a process comprising the steps of: (a) selecting a site on the subject for sensing electrical activity; (b) detecting electrical activity from the site; and (c) filtering the electrical activity within a frequency range of about 0.1 Hertz to about 5 Hertz.
 12. The electrical signal of claim 11, wherein the site on the subject is the forehead. 